收稿：2016-11-11，
录用：2017-9-1，
纸质出版：2018-01
移动端阅览
以高分四号(GF-4)卫星L1级标准分幅数据产品提供高精度的云检测产品为目的，研究针对地球同步轨道卫星数据的云检测算法，改进自动阈值以适应同日不同时刻成像数据的辐射亮度与地表反射特性的变化差异。利用GF-4卫星凝视成像方式获取的同区域序列图像以及云在不同图像上的运动特性，结合自动阈值与Savitzky-Golay(SG)滤波修正检测结果中的误检。算法的两个关键预处理，一是通过自动的几何配准解决未经几何校正的分幅数据之间像素位置对应的问题，二是通过基于典型相关变换自动提取序列图像之间的伪不变特征点集，进而利用相对辐射归一减小了不同时刻成像数据之间的辐射差异。通过内蒙古自治区东部及长江中下游区域70余组数据对算法进行验证，整体上获得了稳定的结果与精度，并且基于序列图像的云检测算法在云边界、高亮地表及薄云区域的检测精度整体优于单幅自动阈值的检测结果。结果表明算法精度上满足GF-4云检测数据产品需求，且算法自动化程度高，便于工程化的数据生产。
The research on cloud detection is an important branch of remote sensing image research. In recent years
with the increasing number and type of remote sensing satellites
cloud detection based on reference map/sequence image has become a subject receiving close review in cloud detection. GF-4 is a geo-synchronous orbit satellite launched by China in December 2015. This satellite is equipped with a visible-light/near-infrared camera with a resolution of 50 m and has typical high-resolution and multi-spectral satellite data characteristics. GF-4 satellites have many common characteristics as meteorological satellites. These common features include the geostationary orbit
area array starring imaging
and the mid-infrared band. GF-4 has the capability to acquire the sequence data of the same area in a short time. This paper attached importance to the algorithm of automatic cloud detection for early GF-4 satellite data acquisition. The research is based on the same area of multiple GF-4 images. First
according to the characteristics of the image data and cloud in different images on the movement characteristics
this work performed automatic geometric registration and relative radiation normalization on multiple images. Then
the image was set to automatic threshold by cloud detection and was processed by the Savitzky–Golay filtering. Finally
this work implemented an automatic cloud detection algorithm for GF-4 sequence images. This paper selected 36 data in the eastern region of Inner Mongolia and 39 data in the middle and lower reaches of the Yangtze River for cloud testing to detect the practical feasibility of the new algorithm. The following preliminary conclusions were obtained. (1) The results of the cloud detection algorithm based on sequence image are superior to those of the single-image cloud detection in terms of overall accuracy. The main difference was observed in the image of the cloud boundary
highlighted surface
and thin cloud area. Through the experiment
this work showed that the algorithm has a high degree of automation and can satisfy the needs of engineering data. (2) Based on the single-image cloud detection
using the automatic threshold method can provide an overall stability for the test results. However
owing to the diversity of the cloud in the image
improving the accuracy is obviously difficult for the proposed algorithm. (3) The mid-infrared band data of GF-4 cannot be simply used for cloud detection due to differences in coverage area
spatial resolution
and acquisition time. The shortcoming of the current algorithm is that the acquisition time of the sequence data is extremely long. Eliminating the radiation difference between the data obtained in the morning and those obtained in the noon with the simple linear relation is difficult. Simultaneously
the follow-up research will focus on the systematic cloud detection accuracy evaluation method.
Chen J, Jönsson P, Tamura M, Gu Z H, Matsushita B and Eklundh L. 2004. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter. Remote Sensing of Environment, 91(3/4): 332–344
Goodman A H and Henderson-Sellers A. 1988. Cloud detection and analysis: A review of recent progress. Atmospheric Research, 21(3/4): 203–228
辜智慧. 2003. 中国农作物复种指数的遥感估算方法研究——基于SPOT/VGT多时相NDVI遥感数据. 北京: 北京师范大学
Gu Z H. 2003. A study of calculating multiple cropping index of crop in China using SPOT/VGT multi-temporal NDVI Data. Beijing: Beijing Normal University
Heidinger A K, Anne V R and Dean C. 2002. Using MODIS to estimate cloud contamination of the AVHRR data record. Journal of Atmospheric and Oceanic Technology, 19(5): 586–601
Hu C M and Tang P. 2011. Automatic algorithm for relative radiometric normalization of data obtained from Landsat TM and HJ-1A/B charge-coupled device sensors. Journal of Applied Remote Sensing, 6(1): 063509
金炜, 俞建定, 符冉迪, 岑雄鹰, 尹曹谦. 2010. 利用密度聚类支持向量机的气象云图云检测. 光电子•激光, 21(7): 1079–1082
Jin W, Yu J D, Fu R D, Cen X Y and Yin C Q. 2010. Meteorological imagery cloud detection using density clustering support vector machine. Journal of Optoelectronics•Laser, 21(7): 1079–1082 (
Lewis J P. 1995. Fast normalized cross-correlation. http://scribblethink.org/Work/nvisionInterface/nip.pdf http://scribblethink.org/Work/nvisionInterface/nip.pdf [2016-11-11]
刘健. 2010. FY-2云检测中动态阈值提取技术改进方法研究. 红外与毫米波学报, 29(4): 288–292
Liu J. 2010. Improvement of dynamic threshold value extraction technic in FY-2 cloud detection. Journal of Infrared and Millimeter Waves, 29(4): 288–292 (
Lowe D G. 2004. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2): 91–110
Lowe D. 2005. Demo code for detecting and matching SIFT features, Version 4, July 6, 2005, http://www.nexoncn.com/read/3470e251dac51e3cf7289acb.html http://www.nexoncn.com/read/3470e251dac51e3cf7289acb.html
卢晶, 薛胜军, 韩阳, 张夏琨, 孙晓娟, 王志伟, 田伟, 杨润芝. 2015. 基于风云3C卫星双氧通道的云检测算法. 科学技术与工程, 15(2): 179–182
Lu J, Xue S J, Han Y, Zhang X K, Sun X J, Wang Z W, Tian W and Yang R Z. 2015. A cloud detection algorithm based on FY-3C satellite double O 2 channels. Science Technology and Engineering, 15(2): 179–182 (
马芳, 张强, 郭妮, 张杰. 2007. 多通道卫星云图云检测方法的研究. 大气科学, 31(1): 119–128
Ma F, Zhang Q, Guo N and Zhang J. 2007. The study of cloud detection with multi-channel data of satellite. Chinese Journal of Atmospheric Sciences, 31(1): 119–128 (
Martinuzzi S, Gould W A and Ramos Gonzalez O M. 2007. Creating cloud-free landsat ETM+ data sets in tropical landscapes: cloud and cloud-shadow removal. General Technical Report IITF-GTR-32, United States Department of Agriculture: 1–18 [DOI: 10.2737/IITF-GTR-32]
Savitzky A, Golay M J E. 1964. Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry, 36(8): 1627–1639
Sedano F, Kempeneers P, Strobl P, Kucera J, Vogt P, Seebach L and San-Miguel-Ayanz J. 2011. A cloud mask methodology for high resolution remote sensing data combining information from high and medium resolution optical sensor. ISPRS Journal of Photogrammetry and Remote Sensing, 66(5): 588–596
单娜, 郑天垚, 王贞松. 2009. 快速高准确度云检测算法及其应用. 遥感学报, 13(6): 1138–1155
Shan N, Zheng T Y and Wang Z S. 2009. High-speed and high-accuracy algorithm for cloud detection and its application. Journal of Remote Sensing, 13(6): 1138–1155 (
Shan X J, Tang P and Hu C M. 2014. An automatic geometric precision correction system based on hierarchical registration for HJ-1 A/B CCD images. International Journal of Remote Sensing, 35(20): 7154–7178
师春香, 瞿建华. 2002. 用神经网络方法对NOAA-AVHRR资料进行云客观分类. 气象学报, 60(2): 250–255
Shi C X and Qu J H. 2002. Cloud classification for NOAA-AVHRR data by using a neural network. Acta Meteorologica Sinica, 60(2): 250–255 (
Stowe L L, Davis P A and McClain E P. 1999. Scientific basis and initial evaluation of the CLAVR-1 global clear/cloud classification algorithm for the advanced very high resolution radiometer. Journal of Atmospheric and Oceanic Technology, 16(6): 656–681
Takayasu H. 1990. Fractals in the physical sciences. Manchester: Manchester University Press
Walder P and Maclaren I. 2000. Neural network based methods for cloud classification on AVHRR images. International Journal of Remote Sensing, 21(8): 1693–1708
王根, 华连生, 刘惠兰, 张苗苗. 2015. 基于最小剩余法的FY-3B/IRAS资料云检测研究. 红外, 36(9): 15–20, 29
Wang G, Hua L S, Liu H L and Zhang M M. 2015. Study of FY-3B/IRAS data cloud detection based on minimum residual method. Infrared, 36(9): 15–20, 29 (
文雄飞, 董新奕, 刘良明. 2009. “云指数法”云检测研究. 武汉大学学报(信息科学版), 34(7): 838–841
Wen X F, Dong X Y and Liu L M. 2009. Cloud index method for cloud detection. Geomatics and Information Science of Wuhan University, 34(7): 838–841 (
闫宇松, 龙腾. 2010. 遥感图像的实时云判技术. 北京理工大学学报, 30(7): 817–821
Yan Y S and Long T. 2010. Real-time cloud detection in optical remote sensing image. Transactions of Beijing Institute of Technology, 30(7): 817–821 (
郁文霞, 曹晓光, 徐琳, Bencherkei M. 2006. 遥感图像云自动检测. 仪器仪表学报, 27(6S): 2184–2186
Yu W X, Cao X G, Xu L and Bencherkei M. 2006. Automatic cloud detection for remote sensing image. Chinese Journal of Scientific Instrument, 27(6S): 2184–2186 (
赵敏, 张荣, 尹东, 王奎. 2012. 一种新的可见光遥感图像云判别算法. 遥感技术与应用, 27(1): 106–110
Zhao M, Zhang R, Yin D and Wang K. 2012. Cloud classification algorithm for optical remote sensing image. Remote Sensing Technology and Application, 27(1): 106–110 (
相关作者
相关机构
京公网安备11010802024621
